Showing papers on "Fuzzy control system published in 2015"
TL;DR: It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds.
Abstract: In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.
475 citations
TL;DR: The main features of the proposed adaptive control approach are that it can guarantee the stability of the closed-loop system, and the tracking errors converge to a small neighborhood of zero, and it can solve the problems of unknown control direction, unknown dead-zone, and unmeasured states simultaneously.
Abstract: In this paper, an adaptive fuzzy backstepping output-feedback tracking control approach is proposed for a class of multi-input and multi-output (MIMO) stochastic nonlinear systems. The MIMO stochastic nonlinear systems under study are assumed to possess unstructured uncertainties, unknown dead-zones, and unknown control directions. By using a linear state transformation, the unknown control coefficients and the unknown slopes characteristic of the dead-zones are lumped together, and the original system is transformed to a new system on which the control design becomes feasible. Fuzzy logic systems are used to approximate the unstructured uncertainties, and a fuzzy state observer is designed to estimate the unmeasured states. By introducing a special Nussbaum gain function into the backstepping control design, a stable adaptive fuzzy output-feedback tracking control scheme is developed. The main features of the proposed adaptive control approach are that it can guarantee the stability of the closed-loop system, and the tracking errors converge to a small neighborhood of zero. Moreover, it can solve the problems of unknown control direction, unknown dead-zone, and unmeasured states simultaneously. Two simulation examples are provided to show the effectiveness of the proposed approach.
410 citations
TL;DR: A new fuzzy controller with the composite parameters adaptive laws are developed and it is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal.
Abstract: In this paper, a composite adaptive fuzzy output-feedback control approach is proposed for a class of single-input and single-output strict-feedback nonlinear systems with unmeasured states and input saturation. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, and a fuzzy state observer is designed to estimate the unmeasured states. By utilizing the designed fuzzy state observer, a serial–parallel estimation model is established. Based on adaptive backstepping dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial–parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws are developed. It is proved that all the signals of the closed-loop system are bounded and the system output can follow the given bounded reference signal. A numerical example and simulation comparisons with previous control methods are provided to show the effectiveness of the proposed approach.
403 citations
TL;DR: It is proved that all the variables of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and also that the observer and tracking errors are guaranteed to converge to a small neighborhood of the origin.
Abstract: In this paper, an adaptive fuzzy decentralized output feedback control design is presented for a class of interconnected nonlinear pure-feedback systems The considered nonlinear systems contain unknown nonlinear uncertainties and the states are not necessary to be measured directly Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and then a fuzzy state observer is designed and the estimations of the immeasurable state variables are obtained Based on the adaptive backstepping dynamic surface control design technique, an adaptive fuzzy decentralized output feedback control scheme is developed It is proved that all the variables of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and also that the observer and tracking errors are guaranteed to converge to a small neighborhood of the origin Some simulation results and comparisons with the existing results are provided to illustrate the effectiveness and merits of the proposed approach
372 citations
TL;DR: A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance and to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model.
Abstract: In this paper, the problem of fuzzy control for nonlinear networked control systems with packet dropouts and parameter uncertainties is studied based on the interval type-2 fuzzy-model-based approach. In the control design, the intermittent data loss existing in the closed-loop system is taken into account. The parameter uncertainties can be represented and captured effectively via the membership functions described by lower and upper membership functions and relative weighting functions. A novel fuzzy state-feedback controller is designed to guarantee the resulting closed-loop system to be stochastically stable with an optimal performance. Furthermore, to make the controller design more flexible, the designed controller does not need to share membership functions and amount of fuzzy rules with the model. Some simulation results are provided to demonstrate the effectiveness of the proposed results.
318 citations
TL;DR: The proposed control method can overcome two problems of linear in the unknown system parameter and explosion of complexity in backstepping-design methods and it does not require that all of the states of the system are measured directly.
Abstract: In this paper, observer and command-filter-based adaptive fuzzy output feedback control is proposed for a class of strict-feedback systems with parametric uncertainties and unmeasured states. First, fuzzy logic systems are used to approximate the unknown and nonlinear functions. Next, a fuzzy state observer is developed to estimate the immeasurable states. Then, command-filtered backstepping control is designed to avoid the explosion of complexity in the backstepping design, and compensating signals are introduced to remove the effect of the errors caused by command filters. The proposed method guarantees that all signals in the closed-loop systems are bounded. The main contributions of this paper are the proposed control method can overcome two problems of linear in the unknown system parameter and explosion of complexity in backstepping-design methods and it does not require that all of the states of the system are measured directly. Finally, two examples are provided to illustrate its effectiveness.
290 citations
TL;DR: A new delay-dependent criterion for L 2 -gain tracking performance of the asynchronous system is derived by applying the deviation bounds of asynchronous normalized membership functions and some criteria on the existence of the fuzzy tracking controller are established.
253 citations
TL;DR: Simulation results show that Generalized Type-2 Fuzzy Controllers outperform their Type-1 and Interval Type- 2 FBuzzy Controller counterparts in the presence of external perturbations.
Abstract: A Generalized Type-2 Fuzzy Controller (GT2FC) was developed.Simulation of a GT2FC for a mobile robot is presented.Experiments support the notion that GT2FC handles more uncertainty. The aim of this paper is to show that a Generalized Type-2 Fuzzy Control System can outperform Type-1 and Interval Type-2 Fuzzy Control Systems when external perturbations are present. A Generalized Type-2 Fuzzy System can handle better uncertainty because of the nature of its membership functions, and as such, they are better tailored for situations where external noise is present. To test the noise resilience of Fuzzy Controllers, the design of a Fuzzy Controller for a mobile robot is presented in this paper, in conjunction with three types of external perturbations: band-limited white noise, pulse noise, and uniform random number noise. Noise resilience is measured through different performance indices, such as ITAE, ITSE, IAE, and ISE. Simulation results show that Generalized Type-2 Fuzzy Controllers outperform their Type-1 and Interval Type-2 Fuzzy Controller counterparts in the presence of external perturbations.
248 citations
TL;DR: By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem and the time-varying delay is approximated by the two-term approximation method.
Abstract: This paper focuses on the problem of fault detection for Takagi–Sugeno fuzzy systems with time-varying delays via delta operator approach. By designing a filter to generate a residual signal, the fault detection problem addressed in this paper can be converted into a filtering problem. The time-varying delay is approximated by the two-term approximation method. Fuzzy augmented fault detection system is constructed in $\delta $ -domain, and a threshold function is given. By applying the scaled small gain theorem and choosing a Lyapunov–Krasovskii functional in $\delta $ -domain, a sufficient condition of asymptotic stability with a prescribed $H_\infty $ disturbance attenuation level is derived for the proposed fault detection system. Then, a solvability condition for the designed fault detection filter is established, with which the desired filter can be obtained by solving a convex optimization problem. Finally, an example is given to demonstrate the feasibility and effectiveness of the proposed method.
224 citations
TL;DR: This paper investigates the adaptive fuzzy backstepping control and H∞ performance analysis for a class of nonlinear systems with sampled and delayed measurements and finds the proposed control scheme and stability analysis to be effective.
Abstract: This paper investigates the adaptive fuzzy backstepping control and ${H_\infty}$ performance analysis for a class of nonlinear systems with sampled and delayed measurements. In the control scheme, a fuzzy-estimator (FE) model is used to estimate the states of the controlled plant, while the fuzzy logic systems are used to approximate the unknown nonlinear functions in the nonlinear system. The controller is obtained based on the FE model by combining the backstepping technique with the classic adaptive fuzzy control method. In the stability analysis, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded (SUUB) and the outputs of the system are proven to converge to a small neighborhood of origin. Furthermore, the ${H_\infty}$ performance is investigated and the outputs of the closed-loop system are bounded in the ${H_\infty}$ sense. Two examples are given to illustrate the effectiveness of the proposed control scheme.
221 citations
TL;DR: Based on the robust control approach, sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed H∞ performance level and the eigenvalues of the filteringerror system in a given circular region.
Abstract: This paper is concerned with the nonfragile distributed $H_\infty$ filtering problem for a class of discrete-time Takagi–Sugeno (T–S) systems in sensor networks. Additive filter gain uncertainties that reflect imprecision in filter implementation are considered. Based on the robust control approach, sufficient conditions are obtained to ensure that the filtering error system is asymptotically stable with a prescribed $H_\infty$ performance level and the eigenvalues of the filtering error system in a given circular region. The filter parameters are determined by solving a set of linear matrix inequalities. A simulation study on the nonlinear tunnel diode circuit system is presented to show the effectiveness of the proposed design method.
TL;DR: The adaptive fuzzy identification and control problems are considered for a class of multi-input multi-output nonlinear systems with unknown functions and unknown dead-zone inputs and the Lyapunov stability theorem is proved.
Abstract: The adaptive fuzzy identification and control problems are considered for a class of multi-input multi-output nonlinear systems with unknown functions and unknown dead-zone inputs. The main characteristics of the considered systems are that 1) they are composed of n subsystems and each subsystem is in nested lower triangular form, 2) dead-zone inputs are in nonsymmetric nonlinear form, and 3) dead-zone inputs appear nonlinearly in the systems and their parameters are not required to be known. The controller design for this class of systems is a difficult and complicated task because of the existences of unknown functions, the couplings among the nested subsystems, and the dead-zone inputs. In the controller design, the fuzzy logic systems are employed to approximate the unknown functions and the differential mean value theorem is used to separate dead-zone inputs. To compensate for dead-zone inputs, the compensative terms are designed in the controllers. The stability of the closed-loop system is proved via the Lyapunov stability theorem. A simulation example is provided to validate the feasibility of the approach.
TL;DR: For a high-order considered system, the attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well.
Abstract: In this paper, the model approximation problem is investigated for a Takagi–Sugeno fuzzy switched system with stochastic disturbance. For a high-order considered system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with a Hankel-norm performance but translates it into a lower dimensional fuzzy switched system as well. By using the average dwell time approach and the piecewise Lyapunov function technique, a sufficient condition is first proposed to guarantee the mean-square exponential stability with a Hankel-norm error performance for the error system. The model approximation is then converted into a convex optimization problem by using a linearization procedure. Finally, simulations are provided to illustrate the effectiveness of the proposed theory.
TL;DR: This paper investigates the problem of model reduction for interval type-2 (IT2) fuzzy systems subject to D stability constraints by introducing some slack matrices and utilizing Lyapunov stability theory to guarantee that the reduced-order model can approximate the original system with an H ∞ performance.
TL;DR: This paper utilizes fuzzy approximation and designed disturbance observers to compensate for the disturbance torques caused by unknown input saturation, fuzzy approximation errors, viscous friction, gravity, and payloads to perform power augmentation tasks of a robotic exoskeleton.
Abstract: To perform power augmentation tasks of a robotic exoskeleton, this paper utilizes fuzzy approximation and designed disturbance observers to compensate for the disturbance torques caused by unknown input saturation, fuzzy approximation errors, viscous friction, gravity, and payloads. The proposed adaptive fuzzy control with updated parameters' mechanism and additional torque inputs by using the disturbance observers are exerted into the robotic exoskeleton via feedforward loops to counteract to the disturbances. Through such an approach, the system does not need any requirement of built-in torque sensing units. In order to validate the proposed framework, extensive experiments are conducted on the upper limb exoskeleton using the state feedback and output feedback control to illustrate the performance of the proposed approaches.
TL;DR: This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index by designing a novel type of IT2 filter such that the filtering error system guarantees the prescribed H∞, L2 -L ∞, passive, and dissipativity performance levels withD stability constraints.
Abstract: This paper investigates the problem of filter design for interval type-2 (IT2) fuzzy systems with D stability constraints based on a new performance index. Attention is focused on solving the $H_{\infty}$ , $L_{2}$ – $L_{\infty}$ , passive, and dissipativity fuzzy filter design problems for IT2 fuzzy systems with D stability constraints in a unified frame. Under the new performance index frame, using Lyapunov stability theory, a novel type of IT2 filter is designed such that the filtering error system guarantees the prescribed $H_{\infty}$ , $L_{2}$ – $L_{\infty}$ , passive, and dissipativity performance levels with D stability constraints. The existence condition of the IT2 filter is expressed as the convex optimization problem, and the filter parameters in the condition can be solved by the standard software. The IT2 fuzzy model and IT2 fuzzy filter do not need to share the same lower and upper membership functions. Finally, a numerical example is provided to show the effectiveness of the proposed results.
TL;DR: A class of new convex reliable stabilization conditions are proposed for T-S fuzzy systems using the properties of fuzzy product inference engines, and the obtained result is extended to the H∞ reliable control case.
Abstract: This paper is concerned with reliable state feedback control synthesis for Takagi and Sugeno (T–S) fuzzy systems with sensor multiplicative faults. By considering the influence of sensor faults on both the system states and premise variables of fuzzy controllers, a class of new convex reliable stabilization conditions are proposed for T–S fuzzy systems using the properties of fuzzy product inference engines. Furthermore, the obtained result is extended to the $H_\infty$ reliable control case. The resulting controllers are reliable in that they provide guaranteed asymptotic stability and $H_\infty$ performance when all sensors are operational, as well as when some sensor experiences failures. Different from the proposed approach, the influence of sensor faults on premise variables is not considered in the existing results; then, it might not guarantee the stability and control performance for T–S fuzzy systems with premise variables dependent on the system states. A numerical example is given to illustrate the effectiveness of the proposed method.
TL;DR: It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible.
Abstract: In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.
TL;DR: This paper presents a proposed new approach for complex control combining several simpler individual fuzzy controllers, which has a hierarchical architecture with 2 levels (individual fuzzy systems and a superior control to adjust the global result).
01 Mar 2015
TL;DR: A new fuzzy approach for diversity control in Ant Colony Optimization through the dynamic variation of parameters and a convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence are presented.
Abstract: Central idea is to avoid or slow down full convergence through the dynamic variation of parameters.Performance of different ACO variants was observed to choose one as the basis to the proposed approach.Convergence fuzzy controller with the objective of maintaining diversity to avoid premature convergence was created. Ant Colony Optimization is a population-based meta-heuristic that exploits a form of past performance memory that is inspired by the foraging behavior of real ants. The behavior of the Ant Colony Optimization algorithm is highly dependent on the values defined for its parameters. Adaptation and parameter control are recurring themes in the field of bio-inspired optimization algorithms. The present paper explores a new fuzzy approach for diversity control in Ant Colony Optimization. The main idea is to avoid or slow down full convergence through the dynamic variation of a particular parameter. The performance of different variants of the Ant Colony Optimization algorithm is analyzed to choose one as the basis to the proposed approach. A convergence fuzzy logic controller with the objective of maintaining diversity at some level to avoid premature convergence is created. Encouraging results on several traveling salesman problem instances and its application to the design of fuzzy controllers, in particular the optimization of membership functions for a unicycle mobile robot trajectory control are presented with the proposed method.
TL;DR: This paper is concerned with the problem of dissipative control for Takagi-Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals and a time-dependent Lyapunov-Krasovskii functional approach is proposed.
Abstract: This paper is concerned with the problem of dissipative control for Takagi–Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals. Based on the time-dependent Lyapunov–Krasovskii functional approach, which makes full use of the available information about the actual sampling pattern, a sufficient condition is established to guarantee the sampled-data systems to be exponentially stable and strictly $(\mathcal {Q},\mathcal {S},\mathcal {R})$ - $\gamma$ -dissipative. Based on the criterion, a design algorithm for the desired sampled-data controller is proposed. The effectiveness and benefits of the results developed in this paper is demonstrated by a controller design for a truck-trailer system.
TL;DR: The fuzzy logic systems are employed to approximate the appropriate unknown functions of the systems, a novel backstepping design procedure is constructively designed, and compensative adaptation laws are provided to compensate for the effects of the dead-zone inputs.
TL;DR: A novel approach to fuzzy sampled-data control of chaotic systems is presented by using a time-dependent Lyapunov functional that makes full use of the information on the piecewise constant input and the actual sampling pattern.
Abstract: In this paper, a novel approach to fuzzy sampled-data control of chaotic systems is presented by using a time-dependent Lyapunov functional. The advantage of the new method is that the Lyapunov functional is continuous at sampling times but not necessarily positive definite inside the sampling intervals. Compared with the existing works, the constructed Lyapunov functional makes full use of the information on the piecewise constant input and the actual sampling pattern. In terms of a new parameterized linear matrix inequality (LMI) technique, a less conservative stabilization condition is derived to guarantee the exponential stability for the closed-loop fuzzy sampled-data system. By solving a set of LMIs, the fuzzy sampled-data controller can be easily obtained. Finally, the chaotic Lorenz system and Rossler’s system are employed to illustrate the feasibility and effectiveness of the proposed method.
TL;DR: This paper is concerned with the problem of the stability analysis and stabilization for Takagi-Sugeno (T-S) fuzzy systems with time delay and uses a recently developed Wirtinger-based integral inequality and introducing slack variables to derive less conservative conditions in terms of linear matrix inequalities (LMIs).
TL;DR: A novel performance index, which is expressed as an extended dissipativity performance, is introduced to be a generalization of H ∞, L2-L∞, passive, and dissipativity performances indexes.
Abstract: This paper is concerned with the problems of state and output feedback control for interval type-2 (IT2) fuzzy systems with mismatched membership functions. The IT2 fuzzy model and the IT2 state and output feedback controllers do not share the same membership functions. A novel performance index, which is expressed as an extended dissipativity performance, is introduced to be a generalization of $H_{\infty }$ , $L_{2}$ – $L_{\infty }$ , passive, and dissipativity performances indexes. First, the IT2 Takagi–Sugeno fuzzy model and the controllers are constructed by considering the mismatched membership functions. Second, on the basis of Lyapunov stability theory, the IT2 fuzzy state and output feedback controllers are designed, respectively, to guarantee that the closed-loop system is asymptotically stable with extended dissipativity performance. The existence conditions of the two kinds of controllers are obtained in terms of convex optimization problems, which can be solved by standard software. Finally, simulation results are provided to illustrate the effectiveness of the proposed methods.
TL;DR: An adaptive fuzzy sliding-mode controller based on the boundary layer approach for speed control of an indirect field-oriented control (IFOC) of an induction motor (IM) drive and an adaptive law is implemented to estimate the unknown bound of uncertainty to minimize the control effort.
Abstract: This paper presents an adaptive fuzzy sliding-mode controller (AFSMC) based on the boundary layer approach for speed control of an indirect field-oriented control (IFOC) of an induction motor (IM) drive. In general, the boundary layer approach leads to a tradeoff between control performances and chattering elimination. To improve the control performances, a fuzzy system is assigned as reaching control part of the fuzzy sliding-mode so that it eliminates the chattering completely in spite of the large uncertainties in the system. The applied fuzzy controller acts like a saturation function with a nonlinear slope inside thin boundary layer near the sliding surface to guarantee the stability of the system. Moreover, an adaptive law is implemented to estimate the unknown bound of uncertainty, which is obtained in the sense of Lyapunov stability theorem to minimize the control effort. The proposed AFSMC-based IM drive is implemented in real-time using DSP board TI TMS320F28335. The experimental and simulation results show the effectiveness of the proposed AFSMC-based IM drive at different operating conditions.
TL;DR: In order to overcome the problem of "explosion of complexity" inherent in the backstepping control design, the dynamic surface control (DSC) technique is introduced into the control scheme.
TL;DR: The results show improved accuracy with lower rule base complexity as well as smaller rule length when using Gen-Smart-EFS, a new methodology for learning evolving fuzzy systems from data streams in terms of on-line regression/system identification problems.
Abstract: In this paper, we propose a new methodology for learning evolving fuzzy systems (EFS) from data streams in terms of on-line regression/system identification problems. It comes with enhanced dynamic complexity reduction steps, acting on model components and on the input structure and by employing generalized fuzzy rules in arbitrarily rotated position. It is thus termed as Gen-Smart-EFS (GS-EFS), short for generalized smart evolving fuzzy systems. Equipped with a new projection concept for high-dimensional kernels onto one-dimensional fuzzy sets, our approach is able to provide equivalent conventional TS fuzzy systems with axis-parallel rules, thus maintaining interpretability when inferring new query samples. The on-line complexity reduction on rule level integrates a new merging concept based on a combined adjacency–homogeneity relation between two clusters (rules). On input structure level, complexity reduction is motivated by a combined statistical-geometric concept and acts in a smooth and soft manner by incrementally adapting feature weights: features may get smoothly out-weighted over time (\(\rightarrow\)soft on-line dimension reduction) but also may become reactivated at a later stage. Out-weighted features will contribute little to the rule evolution criterion, which prevents the generation of unnecessary rules and reduces over-fitting due to curse of dimensionality. The criterion relies on a newly developed re-scaled Mahalanobis distance measure for assuring monotonicity between feature weights and distance values. Gen-Smart-EFS will be evaluated based on high-dimensional real-world data (streaming) sets and compared with other well-known (evolving) fuzzy systems approaches. The results show improved accuracy with lower rule base complexity as well as smaller rule length when using Gen-Smart-EFS.
TL;DR: Results presented in this paper clearly indicate the superiority of the ANFIS model over the fuzzy system, which is a reliable system with relative high degree accuracy.
Abstract: In this work, Dissolved gas Analysis (DGA) has been implemented using softcomputing models namely fuzzy logic and Adaptive Neuro fuzzy inference system (ANFIS). DGA has developed as an effective tool for the identification of transformer incipient faults. A number of standards and procedures have evolved over the years making DGA more reliable and user friendly. A comparative study of the two models has been developed based on their ability to circumvent the limitations of the IEC 599 standard, Rogers ratio method and Doernenburg’s method. The models have been tested using a reported fault database for their diagnostic capability. Results presented in this paper clearly indicate the superiority of the ANFIS model over the fuzzy system. The ANFIS model presents a reliable system with relative high degree accuracy. ANFIS model being very simple to develop can obviate the limitations of conventional methods of transformer fault diagnosis using DGA.
TL;DR: The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin.
Abstract: This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.